# A Web Service for Fragment-based Selectivity Analysis of Drug Leads

> **NIH NIH R43** · CONIFER POINT PHARMACEUTICALS, LLC · 2020 · $225,000

## Abstract

Abstract
Significance: To date, no specific therapeutic drug or vaccine has been approved for the treatment of human coronavirus.
Better, direct-acting anti-viral drugs and accelerated methods for identifying them are desperately needed. Having a large
body of diverse fragment binding simulation data for each SARS-CoV-2 drug target represents a unique opportunity to accelerate
preclinical drug discovery for SARS-CoV-2 protein inhibitors. In contrast to testing-based approaches, understanding fragment
interaction patterns provides chemists specific mechanistic information to guide lead optimization. We propose to (1)
create comprehensive fragment maps for the full suite of SARS-CoV-2 proteins; (2) build automated tools for enumeration and
evaluation of compounds that address protease selectivity and inhibition at Spike protein ppi and allosteric sites; and
(3) make these available worldwide through the BMaps Web application. As such, all anti-viral researchers can benefit.
Innovation: Generating thousands of fragment binding patterns for each of the known SARS-CoV-2 protein structures is a novel
scientific approach to the rational design of SARS-CoV-2 antivirals. This would be the largest data source of fragment data on
SARS-CoV-2 drug targets available and the resource would be accessible by all scientists working to address the COVID-19
pandemic. The innovation proposed is to enable a new scientific approach to rational design for SARS-CoV-2 antivirals based on
the analysis of fragment binding patterns using novel compound enumeration and evaluation methods.
Aim 1: Generate fragment and water maps for the full suite of proteins involved in the coronavirus life cycle. Using hot
spots for location bias, run ~1,000 fragment simulations on each consensus of 6 structures from molecular dynamics.
Aim 2: Develop automated tools to accelerate the enumeration and evaluation of candidate inhibitor molecules. Two
approaches are proposed: (1) adapt our test software to enumerate all available modifications with all fragments for a
given starting point and (2) use a Conditional GAN (Generative Adversarial Network) deep learning network to enumerate
inhibitors from fragments, using discriminator networks to bias towards synthesizable molecules with good properties.
Aim 3. Build a repository of candidate inhibitors targeting coronavirus proteins through a variety of different mechanisms.
Overall Impact: The SARS-CoV-2 protein-fragment maps lead chemists to often non-obvious ideas to progress their compounds
toward clinical trials. The ability to automatically enumerate and evaluate compounds from a large fragment map
repository enables broad access to target-relevant chemical diversity, without tedious manual searching. A repository of
candidate inhibitors targeting coronavirus proteins enables drug researchers to get started quickly.

## Key facts

- **NIH application ID:** 10149527
- **Project number:** 3R43GM133284-01A1S1
- **Recipient organization:** CONIFER POINT PHARMACEUTICALS, LLC
- **Principal Investigator:** John Laurence Kulp III
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $225,000
- **Award type:** 3
- **Project period:** 2020-07-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10149527

## Citation

> US National Institutes of Health, RePORTER application 10149527, A Web Service for Fragment-based Selectivity Analysis of Drug Leads (3R43GM133284-01A1S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10149527. Licensed CC0.

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